Background: The need to deliver interventions targeting multiple diseases in a cost-effective manner calls for integrated disease control efforts. Consequently, maps are required that show where the risk of co-infection is particularly high. Co-infection risk is preferably estimated via Bayesian geostatistical multinomial modelling, using data from surveys screening for multiple infections simultaneously. However, only few surveys have collected this type of data.
Methods: Bayesian geostatistical shared component models (allowing for covariates, disease-specific and shared spatial and non-spatial random effects) are proposed to model the geographical distribution and burden of co-infection risk from single-disease surveys. The ability of the models to capture co-infection risk is assessed on simulated data sets based on multinomial distributions assuming light- and heavy-dependent diseases, and a real data set of Schistosoma mansoni–hookworm co-infection in the region of Man, Côte d'Ivoire. The data were restructured as if obtained from single-disease surveys. The estimated results of co-infection risk, together with independent and multinomial model results, were compared via different validation techniques.
Results: The results showed that shared component models result in more accurate estimates of co-infection risk than models assuming independence in settings of heavy-dependent diseases. The shared spatial random effects are similar to the spatial co-infection random effects of the multinomial model for heavy-dependent data.
Conclusions: In the absence of true co-infection data geostatistical shared component models are able to estimate the spatial patterns and burden of co-infection risk from single-disease survey data, especially in settings of heavy-dependent diseases. Copyright © 2011 John Wiley & Sons, Ltd.
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